It is the subfield of computer science that focuses on creating systems capable of intelligent behavior, including problem solving, learning, adaptation, perception, and language understanding.
The article discusses ACE (Off-Policy Actor-Critic with Causality-Aware Entropy Regularization), a novel reinforcement learning (RL) algorithm. Traditional RL algorithms often overlook the varying significance of distinct primitive behaviors during the…
Continue readingThe paper proposes OmniPred, a framework for training language models as universal regressors over evaluation data from various real-world experiments. This approach, which uses data from Google Vizier, demonstrates that…
Continue readingThe paper introduces FedCQA, a method to answer complex queries on multi-source knowledge graphs (KGs) while preserving privacy. Current methods focus on single KGs and can’t be applied to multiple…
Continue readingThe paper discusses adversarial training’s robustness-accuracy trade-off problem and proposes a solution. The authors focus on invariance regularization to create adversarially invariant representations without losing discriminative power. They identify two…
Continue readingThis article presents a Bayesian approach to off-policy evaluation (OPE) and off-policy learning (OPL) for large action spaces. The authors propose a unified Bayesian framework, sDM, that leverages action correlations…
Continue readingThis article discusses the use of big data analytics to classify earthwork-related locations (ERLs), which are significant sources of urban dust pollution. The researchers used dump truck trajectory, point of…
Continue readingThe article delves into the study of off-policy evaluation (OPE) in environments with complex observations, aiming to develop estimators that can avoid exponential dependence on the horizon. The authors explore…
Continue readingThe paper introduces a novel method for credit card fraud detection, the Causal Temporal Graph Neural Network (CaT-GNN). This method leverages causal invariant learning to reveal inherent correlations within transaction…
Continue readingThe article proposes a way to incorporate expert rules into machine learning models, specifically in the context of concept-based learning. The authors discuss methods to combine logical rules and neural…
Continue readingThe article presents Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a new class of E(p, q)-equivariant CNNs. These networks process multivector fields on pseudo-Euclidean spaces Rp,q, offering significant improvements over baseline methods…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
